Partitioning Spatial Model Uncertainty Based on Joint Spatial Simulation

In this study, an uncertainty analysis procedure for joint sequential simulation of multiple attributes of spatially explicit models used in geographical informational systems was developed based on regression analysis. This procedure utilizes information obtained from joint sequential simulation to establish the relationship between model uncertainty and variation of model inputs. Using this procedure, model variance can be partitioned by model input parameters on a cell by cell basis. In the partitioning, the correlation of neighboring cells is accounted for. With traditional uncertainty analysis methods, this is not possible. In a case study, spatial variation of soil erodibility from a joint sequential simulation of soil properties was analyzed. The results showed that the regression approach is a very effective method in the analysis of the relationship between variation of the model output and model input parameters. It was also shown for the case study that: (1) the uncertainty of soil erodibility of a cell is mainly propagated from its own soil properties; (2) the interactions of soil properties of neighboring cells could reduce uncertainty of soil erodibility; (3) it is sufficient for uncertainty analysis to include the nearest three neighboring cell groups; and (4) the largest uncertainty contributors vary by soil properties and location.     

基于参数不确定性分析的SAR土壤水分反演精度控制方法

参数不确定性是SAR反演土壤水分的重要不确定性来源,为控制土壤水分反演精度,提出一种基于参数不确定性的有效控制土壤水分反演精度的方法,使用该方法可以控制参数的误差范围。首先使用全局敏感性分析方法,确定后向影响散射系数输出的主要参数;在不同量级高斯噪声随机扰动下,将大量各参数采值输入AIEM模型中,得到带噪声的后向散射系数集合;再使用LUT法反演土壤水分,计算反演结果满足误差量级控制范围。以此为基础,利用ENVISAT ASAR双极化数据(VV、VH)和实测土壤水分数据进行验证,利用LUT法反演得到带噪声的土壤水分,计算ASAR影像中采样点土壤水分反演值RMSE0.04cm3/cm3。结果表明各影响参数误差量级控制范围可有效控制土壤水分反演精度,在较大的入射角范围内都适用。     

合成孔径雷达反演裸露地表土壤水分的新方法

提出了一种新的合成孔径雷达(SAR)反演裸露地表土壤水分的经验模型,该模型同时考虑了均方根高度S和相关长度L的影响,并将两个粗糙度参数合二为一,然后利用VV和VH极化的后向散射系数即可反演得到土壤水分。通过实测数据对模型进行了验证,发现在θ020°时,模型反演值与模拟值有着良好的相关关系(R2=0.71)。该模型在不需要测量地面粗糙度的情况下可以反演得到比较好的土壤水分精度,尤其适用于地表情况复杂、难以精确测量的地区。     

Impacts of Spatial Partitioning in Hydroecological Models: Predicting Grassland Productivity with RHESSys

Environmental models constructed with a spatial domain require choices about the representation of space. Decisions in the adaptation of a spatial data model can have significant consequences on the ability to predict environmental function as a result of changes to levels of aggregation of input parameters and scaling issues in the processes being modelled. In some cases, it is possible to construct a systematic framework to evaluate the uncertainty in predictions using different spatial models; in other cases, the realm of possibilities plus the complexity of the environmental model in question may inhibit numeric uncertainty estimates. We demonstrate a range of potential spatial data models to parameterize a landscape‐level hydroecological model (RHESSys). The effects of data model choice are illustrated, both in terms of input parameter distributions and resulting ecophysiological predictions. Predicted productivity varied widely, as a function of both the number of modelling units, and of arbitrary decisions such as the origin of a raster grid. It is therefore important to use as much information about the modelled environment as possible. Combinations of adaptive methods to evaluate distributions of input data, plus knowledge of dominant controls of ecosystem processes, can help evaluate potential representations. In this case, variance‐based delineation of vegetation patches is shown to improve the ability to intelligently choose a patch distribution that minimizes the number of patches, while maintaining a degree of aggregation that does not overly bias the predictions.     

A simple two-band semi-analytical model for retrieval of specific absorption coefficients in coastal waters

A non-linear iterative method is used to replace the traditional spectral slope technique in initializing the total absorption decomposition model. Based on comparison of absorption coefficient by QAA and two-band semi-analytical model (TSAA) models with field measurements collected from the West Florida Shelf waters and Bohai Sea, it is shown that both models are effective in estimating absorption coefficients from the West Florida Shelf waters, but the TSAA model is superior to the QAA model. Use of the TSAA model in estimating absorption coefficient in the West Florida Shelf and Bohai Sea decreases the uncertainty of estimation by 1.3–74.7% from the QAA model. The TSAA model’s sensitivity to the input parameters was evaluated by varying one parameter and keeping the others fixed at their default values. Our results indicate that the TSAA model has quite a strong noise tolerance to addressing the field data of the total absorption coefficient.     

双极化SAR数据反演裸露地表土壤水分

为了较高精度地获取大范围地表土壤水分,提出一种基于双极化合成孔径雷达数据的裸露地表土壤水分反演模型即非线性方程组,通过改进的粒子群算法求解非线性方程组从而得到土壤水分。首先通过AIEM模型数值模拟和回归分析,得到一种新的组合粗糙度,然后模拟分析得到土壤水分与雷达后向散射系数的关系,从而建立雷达后向散射系数与组合粗糙度、土壤水分的经验关系。利用ASAR C波段双极化雷达数据,基于经验关系和改进的粒子群算法即可实现土壤水分的反演。经过黑河流域实测土壤水分数据对模型进行验证,反演结果与实测数据具备良好的相关性(R~2=0.778 6)。与以往同一区域研究成果比较,文中的方法反演精度有所提高,更适用于裸露地表土壤水分反演。     

参数敏感性分析在遥感及生态水文模型中的研究进展

参数敏感性分析SA（Sensitivity Analysis）是遥感、生态和水文模型不确定性分析UA（Uncertainty Analysis）的重要方法之一。本文梳理了遥感散射/辐射模型,以及遥感驱动的生态、水文模型研究中常用的敏感性分析方法,并总结了各类方法的优缺点和适用条件。从识别关键参数、不确定性分析和参数优化3个方面,分析了这些领域中参数敏感性分析研究的进展和存在问题,并介绍了最常用的敏感性分析平台。参数敏感性分析作为模型参数优化的先验知识之一,促进了模型和参数的优化。在不确定性和敏感性矩阵USM（Uncertainty and Sensitivity Matrix）的框架下,结合全局敏感性分析方法开展多阶段遥感反演、参数敏感性的尺度效应、参数敏感性的时空异质性研究更加需要关注。此外,还需要提高敏感性分析的计算效率和模式,来适应未来更加复杂的模型和迅速增长的数据量。     

病态不确定性平差模型的岭估计算法

测量数据在获取的过程中,常存在不确定性,它们会影响参数估计结果,不确定性平差模型的解算方法可以有效提高参数估计的有效性和可靠性。当观测方程的系数矩阵存在接近零的奇异值,采用岭估计可有效抑制观测方程病态性对参数估值结果的影响。当不确定性平差模型出现病态,其受系数矩阵误差和观测值误差的影响更为严重,本文将岭估计法应用于病态不确定性平差模型,推导了迭代算法,以提高解的稳定性,并用算例验证,结果表明了新方法的有效性和可行性。     

冠层反射光谱对植被理化参数的全局敏感性分析

植被理化参数与许多有关植物物质能量交换的生态过程密切相关,定量分析植被反射光谱对理化参数的敏感性是遥感反演理化参数含量的前提。本文采用EFAST(Extended Fourier Amplitude Sensitivity Test)全局敏感性分析方法,利用PROSAIL辐射传输模型分析了冠层疏密程度对叶片生化组分含量、冠层结构以及土壤背景等多种参数敏感性的影响,并对植被理化参数反演所需先验知识的精度问题进行了初步探讨。研究表明:(1)对于较为稠密的冠层,可见光波段的冠层反射率主要受叶绿素含量的影响,近红外和中红外波段的冠层反射率主要受干物质量和含水量的影响;(2)对于稀疏的冠层,LAI是影响400—2500 nm波段范围内冠层反射率的最重要参数,土壤湿度次之,叶片生化参数对冠层反射率的敏感性较低;(3)在已知稀疏冠层LAI的情况下进一步确定土壤的干湿状态,可显著提高冠层反射率对叶绿素含量的敏感度,有助于稀疏冠层叶绿素含量的反演。     

Soil Moisture Estimation in a Vegetated Area Using Combination of AIRSAR and Landsat5-TM Images

High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively.     

尺度驱动的空间聚类理论

空间聚类是探索性空间数据分析的有力手段,不仅可以直接用于发现地理现象的分布格局与分布特征,亦可以为其他空间数据分析任务提供重要的预处理步骤。空间聚类有望成为大数据认知的突破口。空间聚类研究虽然已经引起了广泛关注,但是依然面临两大最根本的困境:"无中生有"和"无从理解"。"无中生有"指的是:绝大多数方法,即使针对不包含聚类结构的数据集,仍然会发现聚类;"无从理解"指的是:即使同一种聚类方法,采用不同的聚类参数就会获得千变万化的聚类结果,而这些结果的含义不明确。造成上述困境的根本原因在于:尺度没有在聚类模型中被当作重要参数而恰当地体现。为此,笔者受到人类视觉多尺度认知原理的启发,根据多尺度表达的"自然法则",建立了一套尺度驱动的空间聚类理论。首先将尺度定量化建模为聚类模型的参数,然后将空间聚类的尺度依赖性建模为一种假设检验问题,最后通过控制尺度参数以自动获得统计显著的多尺度聚类结果。在该理论指导下,可以构建适用不同应用需求的多尺度空间聚类模型,一方面降低了空间聚类过程中的主观性,另一方面有利于对空间聚类模式进行全面而深入的分析。     

带有部分不确定性的平差算法在GPS高程拟合中的应用

观测数据中常包含统计信息未知的不确定性,可能导致所建立的函数模型产生病态,影响参数估计的准确性和可靠性。文中研究GPS高程拟合模型的不确定性,将系数矩阵进行分块,对含有不确定性的区块加以限制,并将不确定度融入函数模型,利用min-max准则,运用带部分不确定性的平差算法(PULS,Least-Squares with Part of Uncertainty)解算拟合参数。实验中选取均匀分布的模拟点坐标及其高程异常值,分别运用最小二乘(LeastSquares,LS)、总体最小二乘(Total Least-Squares,TLS)以及PULS对拟合参数进行解算,结果表明,PULS得到的拟合参数精度高于LS和TLS,说明PULS在GPS高程拟合中应用的有效性。     

时变参数PGM(1, 1)变形预测模型及其应用

在GM(1，1)模型的基础上，考虑参数随时间的变化，用多项式逼近模型参数，同时针对GM(1，1)模型背景值取值方法的不足，引入背景值最佳生成系数，建立了时变参数PGM(1，1)变形预测模型。多项式中的待定系数采用最小二乘法确定，背景值最佳生成系数采用搜索法确定。实例计算表明，时变参数PGM(1，1)变形预测模型具有较高的模拟精度和预测精度，适合用于变形体的变形预测。     

ERS风散射计反演地表参数——一种混合像元方法

基于混合像元的方法,利用ERS风散射计(WSC)数据估算植被覆盖率和同时期NDVI有较高的相关性(0.78),计算出的垂直入射菲涅耳反射系数的空间分布状况也比较合理。     

高斯曲线优化能见度与气溶胶光学厚度转换模型

大气气溶胶是影响对地观测定量精度的最主要不确定性因素。随着定量遥感的发展,对气溶胶光学厚度数据的精度提出了更高要求。在广泛应用的基于辐射传输模型大气校正研究中,需要输入气溶胶光学厚度等关键参数,但与对地观测影像数据同时相的气溶胶光学厚度获取较难,而水平能见度作为表征气溶胶光学特性的间接参数可通过广泛分布的气象台站获得,可将能见度转换得到的气溶胶光学厚度数据作为同时相数据输入传输模型进行大气校正计算。本文以实测的能见度和气溶胶光学厚度数据为基础,通过拟合气溶胶标高其随时间的变化对Peterson模型进行了修正。对修正后的模型进行精度验证得到RMSE为0.254,结果表明优化的模型对精度有较大提升。     

Accounting for Spatial Uncertainty in Optimization with Spatial Decision Support Systems

Spatial decision support systems (SDSS) are designed to make complex resource allocation problems more transparent and to support the design and evaluation of allocation plans. Recent developments in this field focus on the design of allocation plans using optimization techniques. In this paper we analyze how uncertainty in spatial (input) data propagates through, and affects the results of, an optimization model. The optimization model calculates the optimal location for a ski run based on a slope map, which is derived from a digital elevation model (DEM). The uncertainty propagation is a generic method following a Monte Carlo approach, whereby realizations of the spatially correlated DEM error are generated using 'sequential Gaussian simulation'. We successfully applied the methodology to a case study in the Austrian Alps, showing the influence of spatial uncertainty on the optimal location of a ski run and the associated development costs. We also discuss the feasibility of routine incorporation of uncertainty propagation methodologies in an SDSS.     

Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data

In this study, the NIR-red spectral space of Landsat-8 images, which is manifested by a triangle shape, is deployed for developing two new Soil Moisture (SM) indices. First, ten parameters consisting of six distances and four angles were extracted using the position of a random pixel in this triangle. Then, some correlation assessments were made to derive those parameters that were useful for SM estimation, which were five parameters. To build a soil moisture index, all combinations of these five parameters, which were in total 31 different regression equations, were considered, and the best model was named the Triangle Soil Moisture Index (TSMI). The TSMI consists of three parameters. It showed a RMSE of 0.08 and correlation coefficient (R) of 0.67. Since the TSMI does not consider vegetation interface in SM estimation, the Modified TSMI (MTSMI), which takes into account the fraction of soil cover in each pixel, beside those parameters which were used in the TSMI, was developed (MTSMI: RMSE = 0.07, R = 0.74). The results of the TSMI and MTSMI were compared with each other, and with another soil moisture index (SMMRS introduced by Zhan et al. (2007)). It was concluded that the TSMI and MTSMI provide similar results for bare soil or sparsely vegetated surfaces. However, the MTSMI demonstrated a much better performance in densely vegetated surfaces. The accuracy of both the TSMI and MTSMI were significantly higher than the SMMRS. Moreover, the TSMI and MTSMI were validated by comparison with field measured SM data at five different depths. The results showed that satellite estimated SM by these two indices was more correlated with in situ data at 5 cm soil depth compared to other depths. Also, to show the high applicability of the proposed approach for SM estimation, we selected another set of field SM data collected in Australia. The results proved the effectiveness of the method in different study areas.     

A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed

A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.     

CUBE曲面滤波参数联合优选关键技术及应用

CUBE(combined uncertainty and bathymetry estimator)算法是国际上主流的多波束测深异常值自动探测与处理算法,在国内外被广泛应用,但对其核心算法和参数知之甚少,不利于该项技术的国产化。本文详细阐述了CUBE算法的基本原理、数学模型、关键参数和处理步骤,进而建立了CUBE曲面滤波参数联合优选方法。通过选取典型地形区、参数试验、对比分析等步骤完成参数的联合优选,并用台湾浅滩实测数据进行了验证。结果表明,优化后的参数可有效提升多波束数据自动处理的精度和效率。本文成果可应用于国产多波束测深处理软件的深化研发以及多波束实测数据处理。